Optimizing site selection for wind, solar, and hydropower: A comparative analysis of resource sustainability using resources time footprint

Abstract

Achieving carbon neutrality necessitates the integration of renewable energy into existing energy systems. However, most studies on renewable energy site selection focus on individual technologies, often prioritizing a single energy source and failing to account for spatial variability across regions. Here, a novel framework is presented that addresses both site and source selection for distributed renewable energy infrastructure, guided by the principle of resource sustainability. The resource sustainability of small hydropower (SHP), onshore wind power, and rooftop solar photovoltaics (PVs) was compared, and integrated preferential areas were demonstrated. Firstly, the power generation potential of these technologies was estimated, and then their resource sustainability was compared using the resources time footprint (RTF) in terms of materials, CO₂ emissions, land, and labor. The preferential areas for these technologies by applying RTF have been identified as a case study in Fujian, China. The total estimated annual electricity generation potential is 5.1 $\times$10⁴ GWh for onshore wind power and 1.5$\times$10⁵ GWh for rooftop solar PV, while individual SHP plants each generate between 0.4 and 32 GWh in Fujian Province. ∆RTF of material is the largest for solar PVs due to the usage of silver for multi-Si module production. The potential of SHP and onshore wind power to reduce CO₂ emissions is higher than that of solar PV systems. The land occupancy of SHP is greater than that of wind power. ∆RTF of labor is the largest for SHP, followed by wind power and solar PV. Wind power has the smallest average overall ∆RTF (−44.38 years), compared with SHP (−30.38 years) and solar PVs (46.38 years), indicating that wind energy is the most sustainable in the study area. The developed framework offers critical insights for site selection in preliminary planning phases and could be adapted for broader geographic applications, supporting a sustainable energy transition.